Decision intelligence: Optimize the decision-making process

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  Quantumrun Foresight
• November 29, 2022

Insight summary

In a rapidly digitizing world, companies are leveraging decision intelligence technologies to enhance their decision-making, using AI to transform data into actionable insights. This shift is not just about technology; it's also reshaping job roles towards AI management and ethical usage, while heightening concerns about data security and user accessibility. The evolution towards these technologies reflects a broader trend towards data-informed strategies across various industries, posing new challenges and opportunities.

Decision intelligence context

Across industries, companies are integrating more digital tools into their operations and constantly collecting large amounts of data. However, such investments are only worthwhile if they generate actionable results. Some businesses, for example, can make fast and effective decisions using decision intelligence technologies that leverage artificial intelligence (AI) to draw insights from this data and provide more informed decision-making.

Decision intelligence combines AI with business analytics to help organizations make better decisions. Decision intelligence software and platforms allow businesses to make more informed decisions based on data rather than intuition. Accordingly, one of the main benefits of decision intelligence is that it has the potential to simplify the process of drawing insights from data, making it easier for businesses to examine with analytics. Additionally, decision intelligence products could help alleviate the data skills gap by providing insights that do not require a high degree of worker training in analytics or data.

A 2021 Gartner survey stated that 65 percent of the respondents believed their decisions were more complex than in 2019, while 53 percent said there was more pressure to justify or explain their choices. As a result, many multinational companies have prioritized integrating decision intelligence. In 2019, Google hired a chief data scientist, Cassie Kozyrkov, to assist in combining data-led AI tools with behavioral science. Other companies such as IBM, Cisco, SAP, and RBS have also started exploring decision intelligence technologies.

Disruptive impact

One of the most prominent ways in which decision intelligence can help businesses make better decisions is by providing insights into data that would otherwise be unavailable. The programming allows for data analysis that surpasses human limitations by several magnitudes. 

However, a 2022 report by Delloite expressed that accountability is a fundamental trait that supports decision-making on the human side of an enterprise. Highlighting that although decision intelligence is valuable, the goal of an organization should be to be an insight-driven organization (IDO). Delloite stated that an IDO focuses on sensing, analyzing, and acting on the information collected. 

Additionally, decision intelligence technology can help businesses to democratize analytics. Companies without large or sophisticated IT departments can partner with tech firms and startups to reap the benefits of decision intelligence. For instance, in 2020, beverage multinational Molson Coors partnered with decision intelligence company Peak to gain insights into its vast and complex business operations and continually improve service areas.

Implications for decision intelligence

Wider implications of decision intelligence may include: 

• More partnerships between businesses and decision intelligence companies to integrate decision intelligence technologies into their respective business operations.


• Increased demand for decision intelligence experts.


• Increased vulnerability to cyberattacks for organizations. For instance, cybercriminals collecting firms' decision intelligence data or manipulating such platforms in ways that direct companies to take disadvantageous business actions.


• Increasing need for companies to invest in data storage infrastructure so that AI technologies can access large data sets for analysis.


• More AI technologies focusing on UI and UX so that users without advanced tech knowledge can understand and utilize AI technologies.


• Enhanced emphasis on ethical AI development, fostering increased public trust and more stringent regulatory frameworks by governments.


• Shift in employment patterns with more roles focusing on AI oversight and ethical use, reducing demand for traditional data processing jobs.

Questions to consider

• How else might decision intelligence be more effective than the human decision-making process? Or what are other concerns of using decision intelligence?


• Will decision intelligence technologies create a more significant digital divide between large and small-scale companies?